Computational Studies of Model Disordered and Strongly Correlated Electronic Systems

Alexander F. Kemper

Eamonn A. Campbell

Many of the most interesting electronic behaviours currently being studied are associated with strong correlations. In addition, many of these materials are disordered either intrinsically or due to doping. Solving interacting systems exactly is extremely computationally expensive, and approximate techniques developed for strongly correlated systems are not easily adapted to include disorder. As a non-interacting disordered model, it makes sense to consider the Anderson model as a first step in developing an approximate method of solution to the interacting and disordered Anderson-Hubbard model. Our renormalization group (RG) approach is modeled on that proposed by Johri and Bhatt [23]. We found an error in their work which we have corrected in our procedure. After testing the execution of the RG, we benchmarked the density of states and inverse participation ratio results against exact diagonalization. Our approach is significantly faster than exact diagonalization and is most accurate in the limit of strong disorder. Author Keywords: disorder, localization, real-space renormalization, strong correlations

Daniel Q. Duffy

Jianhua Ma, Puhan Zhang, Yaohua Tan +2 more

Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and interatomic forces through examples, thus bypassing the computationally expensive density functional theory calculations. Combining many-body techniques with a deep-learning approach, we demonstrate that a fully connected neural network is able to learn the complex collective behavior of electrons in strongly correlated systems. Specifically, we consider the Anderson-Hubbard (AH) model, which is a canonical system for studying the interplay between electron correlation and strong localization. The ground states of the AH model on a square lattice are obtained using the real-space Gutzwiller method. The obtained solutions are used to train a multitask multilayer neural network, which subsequently can accurately predict quantities such as the local probability of double occupation and the quasiparticle weight, given the disorder potential in the neighborhood as the input.

Hong Yao

Zhang, Zelei, Yan, Jiawei, Lixin Huang +1 more

This work reports a theoretical framework that combines the auxiliary coherent potential approximation (ACPA-DMFT) with dynamical mean-field theory to study strongly correlated and disordered electronic systems with both diagonal and off-diagonal disorders. In this method, by introducing an auxiliary coupling space with extended local degree of freedom,the diagonal and off-diagonal disorders are treated in a unified and self-consistent framework of coherent potential approximation, within which the dynamical mean-field theory is naturally combined to handle the strongly correlated Anderson-Hubbard model. By using this approach, we compute matsubara Green's functions for a simple cubic lattice at finite temperatures and derive impurity spectral functions through the maximum entropy method. Our results reveal the critical influence of off-diagonal disorder on Mott-type metal-insulator transitions. Specifically, a reentrant phenomenon is identified, where the system transitions between insulating and metallic states under varying interaction strengths. The ACPA-DMFT method provides an efficient and robust computational method for exploring the intricate interplay of disorder and strong correlations.